class_docstr: Implements the fixed Multiple Model Adaptive Estimator (MMAE). This
  is a bank of independent Kalman filters. This estimator computes the
  likelihood that each filter is the correct one, and blends their state
  estimates weighted by their likelihood to produce the state estimate.
  
  Parameters
  ----------
  
  filters : list of Kalman filters
      List of Kalman filters.
  
  p : list-like of floats
     Initial probability that each filter is the correct one. In general
     you'd probably set each element to 1./len(p).
  
  dim_x : float
      number of random variables in the state X
  
  H : Measurement matrix
  
  Attributes
  ----------
  x : numpy.array(dim_x, 1)
      Current state estimate. Any call to update() or predict() updates
      this variable.
  
  P : numpy.array(dim_x, dim_x)
      Current state covariance matrix. Any call to update() or predict()
      updates this variable.
  
  x_prior : numpy.array(dim_x, 1)
      Prior (predicted) state estimate. The *_prior and *_post attributes
      are for convienence; they store the  prior and posterior of the
      current epoch. Read Only.
  
  P_prior : numpy.array(dim_x, dim_x)
      Prior (predicted) state covariance matrix. Read Only.
  
  x_post : numpy.array(dim_x, 1)
      Posterior (updated) state estimate. Read Only.
  
  P_post : numpy.array(dim_x, dim_x)
      Posterior (updated) state covariance matrix. Read Only.
  
  z : ndarray
      Last measurement used in update(). Read only.
  
  filters : list of Kalman filters
      List of Kalman filters.
  
  Examples
  --------
  
  ..code:
      ca = make_ca_filter(dt, noise_factor=0.6)
      cv = make_ca_filter(dt, noise_factor=0.6)
      cv.F[:,2] = 0 # remove acceleration term
      cv.P[2,2] = 0
      cv.Q[2,2] = 0
  
      filters = [cv, ca]
      bank = MMAEFilterBank(filters, p=(0.5, 0.5), dim_x=3)
  
      for z in zs:
          bank.predict()
          bank.update(z)
  
  Also, see my book Kalman and Bayesian Filters in Python
  https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
  
  References
  ----------
  
  Zarchan and Musoff. "Fundamentals of Kalman filtering: A Practical
  Approach." AIAA, third edition.
docstr: Predict next position using the Kalman filter state propagation
  equations for each filter in the bank.
  
  Parameters
  ----------
  
  u : np.array
      Optional control vector. If non-zero, it is multiplied by B
      to create the control input into the system.
docstr_tok: ["Predict","next","position","using","the","Kalman","filter","state","propagation","equations","for","each","filter","in","the","bank",".","Parameters","--","--","--","--","--","u",":","np.array","Optional","control","vector",".","If","non-zero",",","it","is","multiplied","by","B","to","create","the","control","input","into","the","system","."]
code: def predict(self, u=0):
      """
      Predict next position using the Kalman filter state propagation
      equations for each filter in the bank.
  
      Parameters
      ----------
  
      u : np.array
          Optional control vector. If non-zero, it is multiplied by B
          to create the control input into the system.
      """
  
      for f in self.filters:
          f.predict(u)
  
      # save prior
      self.x_prior = self.x.copy()
      self.P_prior = self.P.copy()
  
file_path: filterpy/filterpy/kalman/mmae.py
imports: from __future__ import absolute_import, division
  from copy import deepcopy
  import numpy
  from filterpy.common import pretty_str
label_desc: Use image node_id 2 for calling the MMAEFilterBank obj's underlying member method code with example usage: obj.predict(u) without return types